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# Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition

*Nanoscale Research Letters*
**volume 10**, Article number: 405 (2015)

## Abstract

This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures. In this comparative study, 10 greyscale images and 26 black-and-white alphabet characters are tested using the circuit simulator to compare the recognition rate with varying statistical variation and correlation parameters.

As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one, when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.

Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

## Background

Memristors are resistive memories which are based on either interface-switching [1, 2] or filamentary-switching mechanism [3–8]. In the interface-switching mechanism, the interface between the low-resistance region and high-resistance region can be moved according to an applied voltage or current [1, 2]. By doing so, we can control memristance gradually to store analogue value on memristors [1, 2]. However, the materials that show the interface-switching behaviours are rare, and the accuracy in controlling the memristance value is still considered a big concern to be used in practical applications [9]. Moreover, even a small amount of memristance variation can degrade the overall accuracy severely in analogue memristor-based neuromorphic systems [9].

Generally, most of the memristors are known to usually demonstrate the filamentary-switching behaviours, not the interface switching [3–8]. The filamentary-switching memristors can have either a high-resistance state (HRS) or a low-resistance state (LRS) [3–8]. With two states, we can store ‘1’ and ‘0’ on filamentary-switching binary memristors [3–8]. In addition to the fact that filamentary-switching materials are popular, binary memristors with the filamentary switching can be much more tolerant against statistical variations compared to analogue memristors with the interface switching [9]. This is due to the fact that HRS can still be much higher than LRS, in spite of large amounts of statistical variation in LRS and HRS [9]. Thus, for simple neuromorphic applications such as pattern recognition, binary memristor crossbar can be more useful and robust than analogue memristor crossbar in terms of material availability, statistical-variation tolerance, etc. [9].

For implementing the crossbar circuit of pattern recognition, the input image should be compared with the stored images which are already stored in the crossbar. By doing so, the crossbar circuit can calculate amounts of similarity between one input image and many stored images one by one [9, 10]. After comparing the amounts of similarity in the crossbar, the winner-take-all circuit chooses which one the best matches with the input image among many stored images in the crossbar [9, 10].

The detailed operation of crossbar circuit which can perform pattern recognition is explained in Fig. 1a. Here, the solid box represents the input ‘H’ voltage and the open box represents the input ‘L’. Similarly, the solid circle in the array represents the stored ‘LRS’ data and the open circle represents the stored ‘HRS’. If the input ‘H’ is applied to the ‘LRS’ cell, it means that the input pixel matches with the stored data. In Fig. 1a, the input vector of ‘HHLL’ is compared with the four columns which are ‘LRS-LRS-LRS-LRS’, ‘LRS-LRS-LRS-HRS’, ‘LRS-HRS-HRS-LRS’ and ‘LRS-LRS-HRS-HRS’. As you see in Fig. 1a, the fourth column exactly matches with the input vector. However, here, the first, second and fourth columns show the same number of matched cells, as shown in Fig. 1a. The number of matched cells for each column is shown below the array of M^{+} in Fig. 1a. Thus, we cannot decide which column is the best match with the input vector in Fig. 1a.

To solve this problem, we have to use two crossbar arrays, not one, as shown in Fig. 1b [9]. Here, the input vector is applied to M^{+} and its inversion is applied to M^{−} at the same time [9]. The number of the matched cells to the input vector and its inversion is calculated by adding two numbers of the matched cells in M^{+} and M^{−} arrays. Hence, we can decide the best matched column with the input vector among four columns. In Fig. 1b, the best matched column with the input vector of ‘HHLL’ is the fourth column with ‘LRS-LRS-HRS-HRS’. The number of matched cells is as large as 4 for the fourth column.

To design the crossbar circuit with two binary memristor arrays, we can consider two types of crossbar architecture. The first architecture is the complementary crossbar in Fig. 1b [11, 12], and the other one is called the twin crossbar [10]. In this paper, we perform a comparative study between the two crossbar architectures for different variation and correlation parameters using the Monte Carlo method. Based on the results of this comparative study, we can estimate which crossbar architecture is better and how much it is better in various conditions of statistical variation and correlation.

## Methods

### Two Crossbar Architectures Simulated: Complementary Versus Twin Architectures

Figure 2a shows the complementary crossbar architecture which is composed of two memristor arrays of M^{+} and M^{−} [11, 12]. The M^{−} array in Fig. 2a is the inversion of M^{+} array. Here, *a*
_{0} is the input to the first row in M^{+} array. *a*
_{
n−1} is the input to the (*n*−1)th row in M^{+} array. *g*
_{0,0} is the cross-point memristor conductance between the first row and first column in M^{+} array. *g*
_{
n−1,m−1} is the cross-point conductance between the (*n*−1)th row and (*m*−1)th column in M^{+} array. In binary memristors, memristor conductance can be either LRS or HRS. In Fig. 2a, LRS is represented by solid circles and HRS is represented by open circles. *a*
^{’}
_{0} that is the inversion of *a*
_{0} is applied to M^{−} array. Similarly, *g*
^{’}
_{0,0} is the inversion of *g*
_{0,0} in M^{−} array. *y*
^{+}
_{0} is the output of the first column in M^{+} array and y^{−}
_{0} is the output of M^{−} array. *y*
_{0} (*y*
^{+}
_{0} and *y*
^{−}
_{0}) is the amount of similarity between the input vector and the first column in the crossbar. Similarly, *y*
_{
j
} is the result of matching of the input vector with the *j*th column. *y*
_{
j
} can be calculated as follows [10]:

Equation 1 is based on the exclusive NOR function that can calculate the amount of similarity between the input vector and the stored data [10]. If the input from *a*
_{0} to *a*
_{
n−1} is very similar with the stored column from *g*
_{0,j
} to *g*
_{
n−1,j
}, *y*
_{
j
} becomes large. On the contrary, if the input vector is very different from the stored column vector, *y*
_{
j
} becomes small. All *y*
_{
j
} values from *y*
_{0} to *y*
_{
m−1} are compared with each other in the winner-take-all circuit [9, 10]. The largest *y*
_{
j
} among all the *y*
_{
j
} values from *y*
_{0} to *y*
_{
m−1} is chosen by the winner-take-all circuit [9, 10].

To implement the crossbar circuit of pattern recognition, we can consider the other architecture different from the complementary one in Fig. 2a. Figure 2b shows the twin crossbar architecture with two identical M^{+} arrays of binary memristors [10]. Here, the upper M^{+} array is identical with the lower M^{+} array. The twin crossbar architecture was previously proposed by S. N. Truong et al., for low-power image recognition, using the discrete cosine transformation [10]. The twin crossbar architecture is based on Eq. 2 which also performs the exclusive NOR function like Eq. 1 [10].

One distinctive point of Fig. 2b from Fig. 2a is that the twin crossbar does not need to use the complementary M^{+} and M^{−} arrays [9, 10]. Instead of using M^{+} and M^{−} arrays in Fig. 2a, the twin crossbar can use only two identical M^{+} arrays, as shown in Fig. 2b [10]. By doing so, in the twin crossbar architecture, the number of LRS cells can be minimized using some image-processing algorithms such as DCT, because two arrays are identical [10]. One more thing to note here is that the addition of *y*
^{+}
_{
j
} and *y*
^{−}
_{
j
} in Fig. 2a is replaced with the subtraction of *y*
^{+}
_{
j
} and *y*
^{−}
_{
j
} in Fig. 2b [10]. Here, the subtraction in Fig. 2b can be easily implemented using the current mirror circuits [10].

These two different points between the complementary crossbar and the twin one can affect the statistical-variation tolerance of the binary memristor array. To analyse the tolerance quantitatively, this paper performs a comparative study between the two crossbar architectures which are shown in Fig. 2a, b, respectively, for different variation and correlation parameters using the Monte Carlo method. Based on the results of this comparative study, we can estimate which crossbar architecture is better and how much it is better in various conditions of statistical variation and correlation.

### Simulation Set-Up

In this paper, the memristor CMOS hybrid circuits were simulated by Cadence Spectre [13]. Here, memristors were modelled by Verilog-A [14, 15], and CMOS SPICE parameters were obtained from Samsung 0.13-μm CMOS technology. Here, HRS and LRS are assumed 100 MΩ and 10 kΩ, respectively. The supply voltage (*V*
_{DD}) is 1.0 V. The parameters that are used in the statistical simulation with the Monte Carlo method are listed in Table 1.

The statistical simulation was performed using the Monte Carlo method by Cadence Spectre [13]. Here, the percentage variation in memristance was assumed from 10 to 40 %, as shown in Table 1. The statistical distribution function is assumed the Gaussian distribution function. Another important parameter in the statistical simulation is the correlation. As indicated in Fig. 2a, the complementary crossbar has two M^{+} and M^{−} arrays, where M^{−} is the inversion of M^{+} array. With two M^{+} and M^{−} arrays, we can think of both intra-array correlation and inter-array correlation in performing the statistical simulation. Figure 3a shows the inter-array correlation and intra-array correlation in the complementary crossbar, where M^{+} array and M^{−} array are complementary with each other. If the correlation value is 1, it means that all the elements are varied in the same way. When the correlation is 0, all the elements are varied in random. Figure 3b shows the inter-array and intra-array correlations for the twin crossbar which is composed of two identical M^{+} arrays instead of using M^{+} and M^{−} arrays.

Actually, the inter-array and intra-array correlations that are mentioned here are originated from the die-to-die (inter-die) and within-die (intra-die) parameter fluctuations that are very well-known statistical variations that can be found in the semiconductor manufacturing process [16]. The inter-die parameter fluctuations which are caused by various variations of lot-to-lot, wafer-to-wafer and die-to-die can affect every element on a chip equally [16]. In contrast, the intra-die fluctuations consisting of both random and deterministic variations produce non-uniform electrical characteristics across the chip [16]. As one example of these fluctuations, we can think of the variations of photoresist thickness of inter-die and intra-die [16]. We can find that the fluctuation in the photoresist thickness seems random in lot-to lot, wafer-to-wafer and die-to-die [16]. However, the fluctuations within the die can be both deterministic and random. In such a way, the fluctuations of electrical parameters of inter-die and intra-die seem very complicated in the real semiconductor fabrication process. In this paper, using the concepts of the inter-die and intra-die fluctuations in the semiconductor manufacturing process [16], we try to perform the statistical analysis on the inter-array and intra-array variations in memristor crossbar array. Because the fabrication process of memristor crossbars is largely similar with the semiconductor process, we can possibly apply the statistical analysis method of the semiconductor process to the fluctuations of electrical parameters in memristor crossbars.

For the statistical simulation, in this paper, we considered four possible cases of inter-array and intra-array correlations. First, both the inter-array and intra-array correlations are assumed 0. It means that two arrays are not correlated at all and memristance values in each array are random. Second, the inter-array correlation is 0 and the intra-array 1. Here, two arrays are independent, while memristance values in each array are changed in the same way. Third, the inter-array correlation is 1 and the intra-array 0. Two arrays are correlated, but memristance values in each array are varied in random. Fourth, both the inter-array and intra-array correlations are 1. It is understood that the two arrays are correlated and memristance values in each array are varied in the same way.

### Tested Images

In the simulation, we tested 10 greyscale images with 32 × 32 pixels which are shown in Fig. 4a. We also tested 26 black-and-white alphabet characters with 8 × 8 pixels, as shown in Fig. 4b.

### Block Diagram of the New Twin Crossbar

Figure 5a shows a block diagram of the twin crossbar architecture of binary memristors for recognizing 10 greyscale images with 32 × 32 pixels [10]. The conceptual schematic of Fig. 5a was already shown in Fig. 2b. In Fig. 5a, as mentioned just earlier, the input is a greyscale image with 32 × 32 pixels [10]. Hence, the number of input pixels to the crossbar is 32 × 32 = 1024. Each pixel is digitized by 4 bits. Here, *a*
_{0}<0:3> is the 4-bit digitized inputs of *a*
_{0}. /*a*
_{0}<0:3> is the inversion of *a*
_{0}<0:3> [10]. In Fig. 5a, *a*
_{0}<0:3> is applied to the upper M^{+} array and /*a*
_{0}<0:3> is applied to the lower M^{+} array. The upper M^{+} array and the lower M^{+} array are identical to each other in Fig. 5a.

Among the 4-bit digitized signals of *a*
_{0}<0:3>, *a*
_{0}<3> is connected to the column with the weight as large as 8 in the upper M^{+} array. In Fig. 5a, ‘X8’ means the weight is as large as 8 and ‘X1’ means the weight is as small as 1. Similarly, /*a*
_{0}<3> is connected to the column of weight X8 in the lower M^{+} array. Using the simple current mirror circuit, we can perform the weighted summation with four weights of ‘X8’, X4’, ‘X2’ and ‘X1’. *I*
_{0} is the result of weighted summation for the first image, #0, in Fig. 5a. *I*
_{0} can be calculated as 8(*I*
_{0,3}
^{+} − *I*
_{0,3}
^{−}) + 4(*I*
_{0,2}
^{+} − *I*
_{0,2}
^{−}) + 2(*I*
_{0,1}
^{+} − *I*
_{0,1}
^{−}) + (*I*
_{0,0}
^{+} − *I*
_{0,0}
^{−}) for the input image (#0) with 32 × 32 pixels. *a*
_{0} to *a*
_{1023} and /*a*
_{0} to /*a*
_{1023} represent the input image and its inversion with 1024 (32 × 32) pixels. If the input image is similar with the stored image (#0), *I*
_{0} becomes large. If they are very different each other, *I*
_{0} becomes small. In Fig. 5a, we assume that the crossbar stores 10 images from #0 to #9. The amount of *I*
_{1} indicates the similarity between the input image and the stored image (#1). Similarly, *I*
_{9} means the similarity between the stored image (#9) and the input image. The winner-take-all circuit can choose one stored image that is the best match with the input image by comparing the 10 currents from *I*
_{0} to *I*
_{9} in Fig. 5a [10].

Similarly with Fig. 5a, Fig. 5b shows the block diagram of the twin crossbar circuit of binary memristors for recognizing 26 black-and-white alphabet characters with 8 × 8 pixels.

### Training Process of the Crossbar

The training of the crossbar circuit means changing memristor resistance value to HRS or LRS. Here, we can use the 1/2*V*
_{DD} write scheme or 1/3*V*
_{DD} write scheme [17] in training memristors to have the target resistance values of HRS and LRS in this paper. Figure 6a shows the 1/2*V*
_{DD} write scheme, where the selected cell is applied by *V*
_{DD} and GND. Here, the unselected cells on the same row or column with the selected cell are driven by 1/2*V*
_{DD}. If the resistance change due to this 1/2*V*
_{DD} is much smaller than the resistance change due to the full *V*
_{DD}, the unselected cells with 1/2*V*
_{DD} can keep their resistance values unchanged during the training process. If the unselected cells which should be driven by 1/2*V*
_{DD} are very susceptible to this small voltage of 1/2*V*
_{DD}, we can use the 1/3*V*
_{DD} write scheme, as shown in Fig. 6b. In this figure, the selected cell is applied by *V*
_{DD} and GND, like the selected cell in Fig. 6a. However, the unselected cells in Fig. 6b are driven by only 1/3*V*
_{DD} that is much smaller than the 1/2*V*
_{DD} in Fig. 6a [17]. By doing so, we can suppress the unwanted resistance change of the unselected cells in Fig. 6b better than those in Fig. 6a.

## Results and Discussion

In Fig. 7, we simulated the recognition rate for the 10 greyscale images with 32 × 32 pixels. In Fig. 7a, both the inter-array and intra-array correlations are assumed 0. In this case, the two crossbar architectures show the same rate in recognizing the tested images, because two arrays are not correlated with each other and all the memristors in each array have random variation. In Fig. 7b, the inter-array correlation is 0, but the intra-array correlation is 1. It means that two arrays are not correlated with each other, but all the memristors in each array have the correlation as strong as 1. Figure 7b also shows the same recognition rate for both the complementary array and the twin one, as already shown in Fig. 7a. From Fig. 7a, b, we can think that the complementary and twin architectures show the same recognition rate if two arrays are not correlated.

Now, let us see the case of inter-correlation = 1. Figure 7c shows the case of the inter-array correlation = 1 and the intra-array correlation = 0. In this case, the twin crossbar shows better recognition rate than the complementary crossbar. Here, the variation in two identical M^{+} arrays in the twin crossbar can be compensated, because two M^{+} arrays are correlated with each other. However, in the complementary crossbar, the variation in M^{+} array cannot be compensated by the variation in M^{−} array, because M^{−} array is the inversion of M^{+} array. By the same reason, we can also think that the twin crossbar can recognize the tested images better than the complementary crossbar in Fig. 7d. Both the inter-array and intra-array correlations are 1 in Fig. 7d. On average, the twin crossbar shows better recognition rate by 4 % than the complementary one for the inter-array correlation = 1 and intra-array correlation = 0. With the same amounts of variation in memristance, the twin architecture can recognize better by 5.6 % than the complementary one when both the inter-array and intra-array correlations are 1.

In Fig. 8, we simulated the recognition rate for the 26 alphabet characters with 8 × 8 pixels. In more detail, in Fig. 8a the inter-array correlation = 0 and the intra-array correlation = 0. In Fig. 8b, the inter-array correlation = 0 and the intra-array correlation = 1. In Fig. 8c, the inter-array correlation = 1 and the intra-array correlation = 0. In Fig. 8d, the inter-array correlation = 1 and the intra-array correlation = 1. As shown in Fig. 8c, on average, the twin crossbar shows better recognition rate by 4.5 %. With the same amounts of variation in memristance, the twin architecture can recognize better by 6 % than the complementary one when both the inter-array and intra-array correlations are 1, as shown in Fig. 8d.

## Conclusions

This paper performed the comparative study on the statistical-variation tolerance between the two crossbars. Here, we compared the complementary crossbar architecture with the twin architecture for different variation and correlation parameters using the Monte Carlo method in Cadence Spectre software. To perform this comparative study, we tested 10 greyscale images and 26 black-and-white alphabet characters in terms of recognition rate with varying statistical-variation and correlation parameters.

As with the simulation results of 10 greyscale image recognitions, the twin crossbar shows better recognition rate by 4 % on average than the complementary one when the inter-array correlation = 1 and intra-array correlation = 0. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture can recognize better by 5.6 % on average than the complementary one.

Similarly, when the inter-array correlation = 1 and intra-array correlation = 0, the twin architecture can recognize 26 alphabet characters better by 4.5 % on average than the complementary one. When the inter-array correlation = 1 and intra-array correlation = 1, the twin architecture is better by 6 % on average than the complementary one. By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.

## References

- 1.
Park S, Kim H, Choo M, Noh J, Sheri A, Jung S, Seo K, Park J, Kim S, Lee W, Shin J, Lee D, Choi G, Woo J, Cha E, Jang J, Park C, Jeon M, Lee B, Lee BH, Hwang H (2012) RRAM-based synapse for neuromorphic system with pattern recognition function. IEDM Tech Dig 2012:10.2.1–10.2.4

- 2.
Park S, Sheri A, Kim J, Noh J, Jang J, Jeon M, Lee B, Lee BR, Lee BH, Hwang H (2013) Neuromorphic speech systems using advanced ReRAM-based synapse. IEDM Tech Dig 2013:25.6.1–25.6.4

- 3.
Yu S, Wong HSP (2010) Modeling the switching dynamics of programmable-metallization-cell (PMC) memory and its application as synapse device for a neuromorphic computation system. IEDM Tech Dig 2010:22.1.1–22.1.4

- 4.
Kuzum D, Jeyasingh RGD, Wong HSP (2011) Energy efficient programming of nanoelectronic synaptic devices for large-scale implementation of associative and temporal sequence learning. IEDM Tech Dig 2011:30.3.1–30.3.4

- 5.
Suri M, Bichler O, Querlioz D, Cueto O, Perniola L, Sousa V, Vuillaume D, Gamrat C, DeSalvo B (2011) Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. IEDM Tech Dig 2011:4.4.1–4.4.4

- 6.
Yu S, Gao B, Fang Z, Yu H, Kang J, Wong HSP (2012) A neuromorphic visual system using RRAM synaptic devices with sub-pJ energy and tolerance to variability: experimental characterization and large-scale modeling. IEDM Tech Dig 2012:10.4.1–10.4.4

- 7.
Suri M, Bichler O, Querlioz D, Palma G, Vianello E, Vuillaume D, Gamrat C, DeSalvo B (2012) CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (cochlea) and visual (retina) cognitive processing applications. IEDM Tech Dig 2012:10.3.1–10.3.4

- 8.
Suri M, Querlioz D, Bichler O, Palma G, Vianello E, Vuillaume D, Gamrat C, DeSalvo B (2013) Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans Electron Devices 60(7):2402–2409

- 9.
Truong SN, Ham SJ, Min KS (2014) Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition. Nanoscale Res Lett 9:629

- 10.
Truong SN, Shin SH, Byeon SD, Song JS, Min KS: New twin crossbar architecture of binary memristor for low-power image recognition with discrete cosine transform. In preparation, IEEE Trans Nanotechnology, 2015.

- 11.
Mouttet B (2009) Memristor pattern recognition circuit architecture for robotics, Proceeding of the 2nd International Multi-Conference on Engineering and Technological Invitation II., pp 65–70

- 12.
Zhu X, Yang X, Wu C, Wu J, Yi X (2013) Hamming network circuits based on CMOS/memristor hybrid design. IEICE Electronics Express 10(12):1–9

- 13.
Cadence: Virtuoso Spectre Circuit Simulator Reference. Cadence Design System, Inc., San Jose, CA, USA, 2011

- 14.
Ham SJ, Mo HS, Min KS (2013) Low-power V

_{DD}/3 write scheme with inversion coding circuit for complementary memristor array. IEEE Trans Nanotechnol 12(5):851–857 - 15.
Ham SJ, Kim JH, Min KS (2013) Device/circuit co-design guide for passive memristor array with non-linear current–voltage behavior. J Nanosci Nanotechnol 13(9):6451–6454

- 16.
Bowman KA, Duvall SG, Meindl JD (2002) Impact of die-to-die and within-die parameter fluctuations on the maximum clock frequency distribution for gigascale integration. IEEE J Solid-State Circuits 37(2):183–190

- 17.
Mustafa J (2006) Design and analysis of future memories based on switchable resistive elements, Ph.D dissertation. RWTH Aachen University, Germany

## Acknowledgements

The work was financially supported by NRF-2011-0030228, NRF-2013K1A3A1A25038533, NRF-2013R1A1A2A10064812, NRF-2015R1A5A7037615 and BK Plus with the Educational Research Team for Creative Engineers on Material-Device-Circuit Co-Design (Grant No: 22A20130000042), funded by the National Research Foundation of Korea (NRF) and by the Global Scholarship Program for Foreign Graduate Students at Kookmin University. The CAD tools were supported by IC Design Education Center (IDEC), Daejeon, South Korea.

A part of this work was presented at the EMN East Meeting on Energy Materials Nanotechnology, Beijing, China, April 20–23, 2015.

## Author information

## Additional information

### Competing Interests

The authors declare that they have no competing interests.

### Authors’ Contributions

All authors have contributed to the submitted manuscript of the present work. KSM defined the research topic. SNT, SHS, SDB, JSS and HSM designed the circuit and performed the simulation. SNT and KSM wrote the paper. All authors read and approved the submitted manuscript.

### Authors’ Information

SNT is a Ph.D. student at School of Electrical Engineering, Kookmin University, Seoul, South Korea. SHS, SDB and JSS are M.S. students, working at School of Electrical Engineering, Kookmin University, Seoul, South Korea. HSM and KSM are the professors at School of Electrical Engineering, Kookmin University, Seoul, Korea.

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### Cite this article

Truong, S.N., Shin, S., Byeon, S. *et al.* Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition.
*Nanoscale Res Lett* **10, **405 (2015). https://doi.org/10.1186/s11671-015-1106-x

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### Keywords

- Statistical-variation tolerance
- Complementary crossbar
- Twin crossbar
- Binary memristors
- Memristor array
- Pattern recognition